- Data, Statistics and AI for Well-Being of People and Organisations
- Edited by Luigi Fabbris, Alberto Franci, Pietro Renzi
Permutation-based inference for functional data in process optimization
- Elena Barzizza
- Nicolò Biasetton
- Alessandro Fanesi
- Pietro Di Gioia
- Luca Anghileri
- Alberto Dolci
- Luigi Salmaso
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.11
In a food processing context, the temperature at the end of a defrosting process plays a critical role in ensuring product quality and production efficiency. Excessively high temperatures risk compromising product integrity, while low temperatures can delay subsequent processing steps. In collaboration with an industrial partner, a Design of Experiments (DoE) was conducted to identify the optimal configuration of a defrosting system, with the goal of achieving a target final temperature that facilitates smooth transition to the next production phase and minimizes downtime. Temperature measurements were collected using multiple sensors throughout the defrosting cycle, recorded at regular intervals—resulting in data naturally represented as functions over time. This study illustrates the effectiveness and practical applicability of permutation-based statistical inference using the NonParametric Combination (NPC) methodology for the analysis of functional data. Emphasizing the functional nature of the data avoids the loss of information typically associated with the extraction of arbitrary summary features, highlighting the value of functional approaches in industrial experimentation.
- Keywords:
- Functional data,
- NPC,
- Permutation methods,
- Design of Experiment,
University of Padua, Italy - ORCID: 0000-0002-6389-887X
University of Padua, Italy - ORCID: 0000-0002-1123-9899
University of Padua, Italy - ORCID: 0009-0002-3789-0513
Bolton Food S.p.A., Italy
Bolton Food S.p.A., Italy
Bolton Food S.p.A., Italy
University of Padua, Italy - ORCID: 0000-0001-6501-1585
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Chapter Information
Chapter Title
Permutation-based inference for functional data in process optimization
Authors
Elena Barzizza, Nicolò Biasetton, Alessandro Fanesi, Pietro Di Gioia, Luca Anghileri, Alberto Dolci, Luigi Salmaso
Language
English
DOI
10.36253/979-12-215-1062-1.11
Peer Reviewed
Publication Year
2026
Copyright Information
© 2026 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
Data, Statistics and AI for Well-Being of People and Organisations
Editors
Luigi Fabbris, Alberto Franci, Pietro Renzi
Peer Reviewed
Number of Pages
420
Publication Year
2026
Copyright Information
© 2026 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Series Title
Applied Statistics Series